{"id":94960,"date":"2020-10-21T22:26:22","date_gmt":"2020-10-21T19:26:22","guid":{"rendered":"https:\/\/en.buradabiliyorum.com\/novel-method-for-measuring-spatial-dependencies-turns-less-data-into-more-data\/"},"modified":"2020-10-21T22:26:22","modified_gmt":"2020-10-21T19:26:22","slug":"novel-method-for-measuring-spatial-dependencies-turns-less-data-into-more-data","status":"publish","type":"post","link":"https:\/\/buradabiliyorum.com\/en\/novel-method-for-measuring-spatial-dependencies-turns-less-data-into-more-data\/","title":{"rendered":"#Novel method for measuring spatial dependencies turns less data into more data"},"content":{"rendered":"

“#Novel method for measuring spatial dependencies turns less data into more data<\/strong>”<\/p>\n

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\n Depiction of interdependencies in human migration away from flood zones to inland cities in Bangladesh. Credit: Maurizio Porfiri, Ph.D.
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The identification of human migration driven by climate change, the spread of COVID-19, agricultural trends, and socioeconomic problems in neighboring regions depends on data\u2014the more complex the model, the more data is required to understand such spatially distributed phenomena. However, reliable data is often expensive and difficult to obtain, or too sparse to allow for accurate predictions.<\/p>\n


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Maurizio Porfiri, Institute Professor of mechanical and aerospace, biomedical, and civil and urban engineering and a member of the Center for Urban Science<\/a> and Progress (CUSP) at the NYU Tandon School of Engineering, devised a novel solution based on network and information theory that makes “little data” act big through, the app<\/a>lication of mathematical techniques normally used for time-series<\/a>, to spatial processes.<\/p>\n

The study, “An information-theoretic approach to study spatial dependencies in small datasets,” featured on the cover of Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences<\/i>, describes how, from a small sample of attributes in a limited number of locations, observers can make robust inferences of influences, including interpolations to intermedia<\/a>te areas or even distant regions that share similar key attributes.<\/p>\n

“Most of the time the data sets are poor,” Porfiri explained. “Therefore, we took a very basic approach, applying information theory to explore whether influence in the temporal sense could be extended to space, which allows us to work with a very small data set, between 25 and 50 observations,” he said. “We are taking one snapshot of the data and drawing connections\u2014not based on cause-and-effect, but on interaction between the individual points\u2014to see if there is some form of underlying, collective response in the system.”<\/p>\n

The method, developed by Porfiri and collaborator Manuel Ruiz Mar\u00edn of the Department of Quantitative Methods, Law and Modern Languages, Technical University of Cartagena, Spain, involved:<\/p>\n